By Topic

A simple particle swarm optimization combined with chaotic search

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Chunxia Fan ; Coll. of Autom., Nanjing Univ. of Posts & Telecommun., Nanjing ; Guoping Jiang

The particle swarm optimization algorithm with constriction factor (CFPSO) has some demerits, such as relapsing into local extremum, slow convergence velocity and low convergence precision in the late evolutionary. A chaotic optimization-based simple particle swarm optimization equation with constriction factor is developed. Piecewise linear chaotic map is employed to perform chaotic optimization due to its ergodicity and stochasticity. Consequently, the particles are accelerated to overstep the local extremum in sCFPSO algorithm. The experiment results of six classic benchmark functions show that the proposed algorithm improves extraordinarily the convergence velocity and precision in evolutionary optimization, and can break away efficiently from the local extremum. Furthermore, the algorithm obtains better optimization results with smaller populations and evolutionary generations. Therefore, the proposed algorithm improves the practicality of the particle swarm optimization.

Published in:

Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on

Date of Conference:

25-27 June 2008